Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science

Concepts and Assumptions

Jonathan Snowden, Ellen Tilden, Michelle C. Odden

Research output: Contribution to journalReview article

3 Citations (Scopus)

Abstract

In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect.

Original languageEnglish (US)
Pages (from-to)721-730
Number of pages10
JournalJournal of Midwifery and Women's Health
Volume63
Issue number6
DOIs
StatePublished - Nov 1 2018

Fingerprint

Research Personnel
Parturition
Epidural Analgesia
Postpartum Hemorrhage
Research
Causality
Randomized Controlled Trials

Keywords

  • assumptions
  • causal inference framework
  • midwifery science
  • observational studies
  • physiologic childbearing science
  • temporality

ASJC Scopus subject areas

  • Obstetrics and Gynecology
  • Maternity and Midwifery

Cite this

Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science : Concepts and Assumptions. / Snowden, Jonathan; Tilden, Ellen; Odden, Michelle C.

In: Journal of Midwifery and Women's Health, Vol. 63, No. 6, 01.11.2018, p. 721-730.

Research output: Contribution to journalReview article

@article{f98543ea87964e41bc6b700bdeecb634,
title = "Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science: Concepts and Assumptions",
abstract = "In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect.",
keywords = "assumptions, causal inference framework, midwifery science, observational studies, physiologic childbearing science, temporality",
author = "Jonathan Snowden and Ellen Tilden and Odden, {Michelle C.}",
year = "2018",
month = "11",
day = "1",
doi = "10.1111/jmwh.12868",
language = "English (US)",
volume = "63",
pages = "721--730",
journal = "Journal of Midwifery and Women's Health",
issn = "1526-9523",
publisher = "Wiley-Blackwell",
number = "6",

}

TY - JOUR

T1 - Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science

T2 - Concepts and Assumptions

AU - Snowden, Jonathan

AU - Tilden, Ellen

AU - Odden, Michelle C.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect.

AB - In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect.

KW - assumptions

KW - causal inference framework

KW - midwifery science

KW - observational studies

KW - physiologic childbearing science

KW - temporality

UR - http://www.scopus.com/inward/record.url?scp=85056543182&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056543182&partnerID=8YFLogxK

U2 - 10.1111/jmwh.12868

DO - 10.1111/jmwh.12868

M3 - Review article

VL - 63

SP - 721

EP - 730

JO - Journal of Midwifery and Women's Health

JF - Journal of Midwifery and Women's Health

SN - 1526-9523

IS - 6

ER -